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All Journal International Journal of Electrical and Computer Engineering Mechatronics, Electrical Power, and Vehicular Technology Perfecting a Video Game with Game Metrics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology JURNAL NASIONAL TEKNIK ELEKTRO Techne : Jurnal Ilmiah Elektroteknika JAREE (Journal on Advanced Research in Electrical Engineering)
Mohammad Zaenal Efendi
Program Studi Teknik Elektro Industri, Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Balancing Charging System Using Adaptive Neuro-Fuzzy Inference System Based On CUK Converter Mohammad Fajar Setyawan; Mohammad Zaenal Efendi; Farid Dwi Murdianto
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 2 (2021): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i2.199

Abstract

In a battery set, there is always a voltage difference caused by charging and discharging. Therefore, it is necessary to pay attention to the condition of the battery or State of Charge (SOC) so that it is in a balanced state between the batteries. Unbalanced battery conditions result in decreased performance of the battery. For that we need a balancing circuit that works actively with the help of a DC-DC converter. DC-DC converters generally have a principle like a buck-boost converter to increase and decrease the output voltage, however the output still has a fairly large ripple in the waveform. Therefore, the CUK converter is used which is a development of the buck-boost converter topology, where the output of this CUK converter has smaller ripples because it uses two capacitors and two inductors. Of the various methods used to adjust the duty cycle of the CUK converter, a precise and accurate algorithm is needed to overcome the instability of the converter output. The method used to adjust the duty cycle uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm as the development of the Fuzzy method. The system is implemented using MATLAB Simulink software. The simulation results show that the output of the CUK converter with the ANFIS method has a faster response speed reaching a set point of 1.95 × 10-4 seconds and the accuracy of the output voltage with ANFIS is 99.94% while the accuracy of the output converter current using ANFIS is 65.7%.Keywords: ANFIS, balancing, battery, CUK converter, state of charge (SOC).15
CC-CV Controlled Fast Charging Using Fuzzy Type-2 for Battery Lithium-Ion Ahmad Zidan Falih; Mohammad Zaenal Efendi; Farid Dwi Murdianto
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 2 (2021): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i2.200

Abstract

Energy dependency is increasing along with the increase in population growth rate, while the fossil energy is decreasing. Alternative energy such as solar energy is one solution to provide renewable energy, but solar energy cannot provide an intense supply of energy. Therefore, the equipment needs an energy storage. The battery has important role in energy storage with the performance of the battery that need an attention. The method and type of battery used  must be considered to maintain battery lifetime and  reduce overcharging. The purpose of this research is to understand the process of fast charging the CC-CV (Constant Current Constant Voltage) method on Lithium-Ion battery which is expected to reduce battery overcharging. In this method, the current is maintained constant until certain conditions then followed by constant voltage to prevent overcharging. The voltage from the solar panel is very high, voltage reduction is needed as the charging voltage for the battery. The DC-DC Converter used is Buck Converter which is given Fuzzy Type-2 algorithm to maintain a current of 10 Ampere during CC conditions and  a voltage of 14.4 Volt during CV conditions with switch of CC conditions to CV conditions on SoC 99.25%.Keywords: battery charging, buck converter, CC-CV, lithium-ion, type-2 fuzzy.
Prototipe Power Supply Gate driver untuk Multilevel Inverter dengan Menggunakan Flyback Converter Multi Output Novie Ayub Windarko; Akhmad Puryanto; Rachma Prilian Eviningsih; Moh. Zaenal Efendi; Eka Prasetyono; Bambang Sumantri
Techné : Jurnal Ilmiah Elektroteknika Vol. 19 No. 1 (2020)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1110.657 KB) | DOI: 10.31358/techne.v19i01.219

Abstract

Dengan perkembangan konverter elektronika daya, kebutuhan untuk memperkecil ukuran dan keandalan semakin meningkat. Dengan meningkatnya frekuensi switching pada konverter, maka dv/dt yang tinggi dapat menyebabkan kesalahan dalam turn-on ataupun turn-off switching devices. Masalah-masalah tersebut dapat diatasi dengan menerapkan tegangan bias negatif untuk melakukan turn-off switching devices. Makalah ini mengusulkan prototipe power supply gate driver yang dikhususkan untuk Multi Level Inverter (MLI). Sebuah prototipe power supply gate driver yang menggunakan satu unit konverter flyback dengan multi output untuk tegangan bias positif dan negatif, serta terisolasi galvanis sebanyak switching devices MLI. Dengan menggunakan topologi dasar flyback maka konverter ini memiliki isolasi galvanis melalui trafo frekuensi tinggi. Dari hasil eksperimen proses switching bisa berubah dari kondisi dari on menuju off dan dari off menuju on bisa berjalan secara sempurna.
Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading Novie Ayub Windarko; Evi Nafiatus Sholikhah; Muhammad Nizar Habibi; Eka Prasetyono; Bambang Sumantri; Moh. Zaenal Efendi; Hazlie Mokhlis
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4571-4585

Abstract

Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.
Maximum Power Point Tracking dengan Metode Modified Human Psychology Optimization pada Kondisi Partial Shading MOH. ZAENAL EFENDI; LUCKY PRADIGTA SETIYA RAHARJA; MOCHAMMAD RODY DWIRANTONO; FEBY CHANDRA ARSANDI; NABILA LUTFIAH
Jurnal Elkomika Vol 10, No 4 (2022): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i4.976

Abstract

ABSTRAKPanel surya memiliki kemungkinan tertutup saat menerima sinar matahari sehingga terjadi kondisi partial shading yang berpengaruh pada daya panel surya. Kita dapat memaksimalkan daya keluaran panel surya dengan Metode Maximum Power Point Tracking (MPPT). MPPT konvensional memiliki kemungkinan terjebak pada kondisi Local Peak (LP) sehingga daya yang dihasilkan tidak maksimal. Untuk mengatasi hal itu digunakan metode Modified Human Psychology Optimization (MHPO) pada kondisi partial shading sehingga MPPT dapat mencapai Global Peak (GP). Metode MHPO diterapkan pada Flyback Converter untuk melihat performansinya. Metode MHPO memiliki akurasi rata-rata di atas 98,75% dan waktu tracking yaitu 0,29 detik. Metode MHPO dapat mencapai kenaikan daya maksimum mencapai 45,57% dibandingkan dengan metode Human Psychology Optimization (HPO). Dengan metode MHPO dapat meningkatkan hasil keluaran daya panel surya pada kondisi partial shading.Kata kunci: Panel Surya; Partial Shading; MPPT; MHPO; Flyback Converter. ABSTRACTSolar panels have the possibility of being closed when receiving sunlight so that partial shading conditions occur which affect the power of the solar panels. We can maximize the output power of solar panels with the Maximum Power Point Tracking (MPPT) Method. Conventional MPPT has the possibility of being trapped in the Local Peak (LP) condition so that the power generated is not optimal. To overcome this problem, the Modified Human Psychology Optimization (MHPO) method is used in partial shading conditions so that MPPT can reach Global Peak (GP). The MHPO method is applied to the Flyback Converter to see its performance. The MHPO method has an average accuracy of over 98.75% and a tracking time of 0.29 seconds. The MHPO method can achieve a maximum power increase of 45.57% compared to the Human Psychology Optimization (HPO) method. The MHPO method can increase the output power of solar panels in partial shading conditions.Keywords: Solar Panels; Partial Shading; MPPT; MHPO; Flyback Converter.
Perbandingan Performa Metode Maximum Power Point Tracking Human Psychology Optimization (HPO), Artificial Bee Colony (ABC) dan Fuzzy Logic Controller (FLC) pada Flyback Converter Kondisi Parsial Shading Moh. Zaenal Efendi; Mochammad Rody Dwirantono; Suhariningsih Suhariningsih; Lucky Raharja
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1022.2023

Abstract

Maximum Power Point Tracking (MPPT) is a method to track the power point of an energy source with the intention to generate maximum power. The surface of the Solar Panel has the possibility of being blocked when it receives sunlight. The barrier can be in the shape of shadows of objects that are nearby solar panels. The problem causes the power generated to be not optimal and makes more than one MPPT peak on the characteristics of P-V. This paper compares several methods of MPPT such as Human Psychology Optimization (HPO), Artificial Bee Colony (ABC), and Fuzzy logic Controller (FLC) under partial shading conditions, the comparison of three method by simulation. This algorithm hooks up to a flyback converter to provide MPP. From the results of MPPT accuracy in partial shading situations, the ABC and HPO approach methods can achieve GMPP with more than 82.22 % accuracy. For convergence, ABC needs extra time to discover GMPP. From the results, the Fuzzy approach can track however nevertheless trapped on LMPP.
A Comparison of Type-1 and Type-2 Fuzzy Logic Controller for Full Bridge Boost Converter on DC Microgrid System MOH. ZAENAL EFENDI; NUR SHINTA ROMADLONIYAH; RACHMA PRILIAN EVININGSIH; NOVIE AYUB WINDARKO
Jurnal Elkomika Vol 11, No 4 (2023): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.1046

Abstract

ABSTRAKDengan meningkatnya kebutuhan listrik, penurunan pasokan energi fosil, serta sulitnya pendistribusian listrik ke daerah terpencil merupakan beberapa masalah yang mendesak. Energi matahari melalui panel surya dapat digunakan untuk mendukung sistem DC Microgrid serta cocok untuk jaringan listrik skala kecil. Full Bridge Boost Converter dengan transformator frekuensi tinggi yang dikendalikan oleh Fuzzy Logic Type-1 (T1FL) dan Fuzzy Logic Type-2 (T2FL) merupakan salah satu pilihan yang dapat dilakukan untuk memaksimalkan pemanfaatan energi matahari sehingga dapat meningkatkan efisiensi serta keandalan sistem pada DC Microgrid dengan menjaga tegangan keluaran menjadi konstan. Dari hasil pengujian dapat diketahui bahwa dengan menggunakan T2FL dapat menjaga tegangan keluaran Full Bridge Boost Converter dapat mencapai tegangan setpoint 320V dengan kesalahan sebesar 0.16% dan stabil dalam 0.59742ms. Sementara, T1FL memerlukan 0.7161ms untuk mencapai setpoint dengan kesalahan 2.8%.Kata kunci: full bridge boost converter, T2FL, T1FL, DC Microgrid ABSTRACTThe increasing electricity demand, the decreasing supply of fossil energy, and the difficulty in distributing electricity to remote areas are some of the urgent problems. Solar energy through solar panels can be used to support DC Microgrid systems and is suitable for small-scale power grids. Full Bridge Boost Converter with high-frequency transformers controlled by Fuzzy Logic Type-1 (T1FL) and Fuzzy Logic Type-2 (T2FL) is one of the choices that can be made to maximize the use of solar energy to increase the efficiency and reliability of systems on DC Microgrids by keeping the output voltage constant. From the test results, it can be seen that using T2FL can maintain the output voltage of the Full Bridge Boost Converter which can reach a setpoint voltage of 320V with an error of 0.16% and is stable within 0.59742ms. Meanwhile, T1FL takes 0.7161ms to reach the setpoint with an error of 2.8%.Keywords: full bridge boost converter, T2FL, T1FL, DC Microgrid
Co-Authors Abdul Rizal Abdul Rizal, Abdul Ahmad Zidan Falih Akhmad Puryanto Aldi Erzanuari Aldi Erzanuari, Aldi Annas Budi Prastyawan Bambang Sumantri Eka Prasetyono, Eka Evi Nafiatus Sholikhah Farid Dwi Murdianto FEBY CHANDRA ARSANDI Fito Ardli Fitri Habibi, Muhammad Nizar Hazlie Mokhlis Lucky Pradigta Setiya Raharja Lucky Raharja Luluk Badriyah MOCHAMMAD RODY DWIRANTONO Moh. Faisal Amir Moh. Faisal Amir Moh. Ifaldi Akbar Mohammad Fajar Setyawan NABILA LUTFIAH Novie Ayub Windarko NOVIE AYUB WINDARKO NUR SHINTA ROMADLONIYAH Rachma Prilian Eviningsih RACHMA PRILIAN EVININGSIH Suhariningsih Suhariningsih Suryono . Suryono Suryono Taufik Hidayat Wicaksana, Ragil Wigas